| 1 |
RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
|
|
|
|
In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
From FreEM to D'AlemBERT ; From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French
|
|
|
|
In: Proceedings of the 13th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-03596653 ; Proceedings of the 13th Language Resources and Evaluation Conference, European Language Resources Association, Jun 2022, Marseille, France (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
|
|
|
|
In: Techniques de l'Ingenieur ; https://hal.archives-ouvertes.fr/hal-03619077 ; Techniques de l'Ingenieur, Techniques de l'ingénieur, 2022, ⟨10.51257/a-v1-in195⟩ ; https://www.techniques-ingenieur.fr/base-documentaire/innovation-th10/innovations-en-electronique-et-tic-42257210/transformer-des-reseaux-de-neurones-pour-le-traitement-automatique-des-langues-in195/ (2022)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
|
|
|
|
In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Imputing out-of-vocabulary embeddings with LOVE makes language models robust with little cost
|
|
|
|
In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Structured, flexible, and robust: comparing linguistic plans and explanations generated by humans and large language models ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages ...
|
|
Chen, Fuxiang. - : Federated Research Data Repository / dépôt fédéré de données de recherche, 2022
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Sentence Level Embedding Detoxification via Toxic Component Removal ...
|
|
: University of Virginia, 2022
|
|
BASE
|
|
Show details
|
|
|
| 9 |
MIss RoBERTa WiLDe: Metaphor Identification Using Masked Language Model with Wiktionary Lexical Definitions
|
|
|
|
In: Applied Sciences; Volume 12; Issue 4; Pages: 2081 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning
|
|
|
|
In: Applied Sciences; Volume 12; Issue 9; Pages: 4099 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
|
|
|
|
In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification
|
|
|
|
In: Applied Sciences; Volume 12; Issue 4; Pages: 2185 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages
|
|
|
|
In: Future Internet; Volume 14; Issue 3; Pages: 69 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Correcting Diacritics and Typos with a ByT5 Transformer Model
|
|
|
|
In: Applied Sciences; Volume 12; Issue 5; Pages: 2636 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Language Competition and Language Shift in Friuli-Venezia Giulia: Projection and Trajectory for the Number of Friulian Speakers to 2050
|
|
|
|
In: Sustainability; Volume 14; Issue 6; Pages: 3319 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
An Information Theoretic Approach to Symbolic Learning in Synthetic Languages
|
|
|
|
In: Entropy; Volume 24; Issue 2; Pages: 259 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Comparison of Text Mining Models for Food and Dietary Constituent Named-Entity Recognition
|
|
|
|
In: Machine Learning and Knowledge Extraction; Volume 4; Issue 1; Pages: 254-275 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Language and vision in conceptual processing: Multilevel analysis and statistical power ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics.
|
|
|
|
Abstract:
Semantic knowledge about individual entities (i.e., the referents of proper names such as Jacinta Ardern) is fine-grained, episodic, and strongly social in nature, when compared with knowledge about generic entities (the referents of common nouns such as politician). We investigate the semantic representations of individual entities in the brain; and for the first time we approach this question using both neural data, in the form of newly-acquired EEG data, and distributional models of word meaning, employing them to isolate semantic information regarding individual entities in the brain. We ran two sets of analyses. The first set of analyses is only concerned with the evoked responses to individual entities and their categories. We find that it is possible to classify them according to both their coarse and their fine-grained category at appropriate timepoints, but that it is hard to map representational information learned from individuals to their categories. In the second set of analyses, we learn to decode from evoked responses to distributional word vectors. These results indicate that such a mapping can be learnt successfully: this counts not only as a demonstration that representations of individuals can be discriminated in EEG responses, but also as a first brain-based validation of distributional semantic models as representations of individual entities. Finally, in-depth analyses of the decoder performance provide additional evidence that the referents of proper names and categories have little in common when it comes to their representation in the brain.
|
|
Keyword:
brain decoding; categories; distributional semantics; EEG; individual entities; language models; proper names
|
|
URL: https://doi.org/10.3389/frai.2022.796793
https://qmro.qmul.ac.uk/xmlui/handle/123456789/77431
|
|
BASE
|
|
Hide details
|
|
|
|
|
